Comparison of rigid and deformable coregistration between mpMRI and CT images in radiotherapy of prostate bed cancer recurrence

PURPOSE

To evaluate the accuracy of rigid coregistration between multiparametric magnetic resonance (mpMR) and computed tomography (CT) images for radiotherapy of prostate bed cancer recurrence.

MATERIALS AND METHOD

Fifty-three patients (59 nodules) accrued in a prospective study on salvage radiotherapy for prostatic bed recurrence were suitable for the analysis. Patients underwent a pre radiotherapy mpMR exam and a planning CT in the same treatment position and with control of organ filling. The site of recurrence was delineated on mpMR images and contours transferred on planning CT images using both rigid and deformable registrations. Coregistrations were evaluated by mathematical operators that quantify deformation (Jacobian determinant and vector curl) and similarity indices (Dice and Jaccard coefficients). Dose coverage was evaluated.

RESULTS

Deformable registration did not change volumes, (p = 0.92 MW test). The Jacobian coefficient and the vector curl revealed no important image deformations. Dice and Jaccard coefficients indicated dislocation of the nodule volumes. Dislocation magnitude was d = (5.6 ± 3.1) mm. Organ filling was not correlated with deformation or dislocation. Volumes were covered by the 95% isodose in 96% of cases when rigid registration was performed versus 75% of cases when deformed.

CONCLUSIONS

Rigid image coregistration is sufficiently accurate in this setting. The results indicate that the deformable registration tends to shrink the voxels and to dislocate the ROI, the adopted expansion for the recurrence volume adequately accounts for the observed deformation and dislocation, provided that organ filling is controlled.

Targeted Alpha Therapy in mCRPC (Metastatic Castration-Resistant Prostate Cancer) Patients: Predictive Dosimetry and Toxicity Modeling of 225Ac-PSMA (Prostate-Specific Membrane Antigen)

Radioligand therapy is a type of internal radiotherapy combining a short-range radioisotope labeled to a carrier with a high affinity for a specific receptor expressed on tumor cells. Targeted alpha therapy (TAT) combines a high-linear energy transfer (LET) emitter (²²⁵Ac) with a prostate-specific membrane antigen (PSMA) carrier, specifically binding tumor cells in patients with metastatic castration-resistant prostate cancer. Although the antitumor activity of ²²⁵Ac-PSMA is well-documented, this treatment is nowadays only used as salvage therapy because the high incidence of xerostomia limits the therapeutic window. Thus, methods to reduce salivary toxicity and models able to describe xerostomia incidence are needed. We recently studied the efficacy of salivary gland protectors administered in combination with ¹⁷⁷Lu-PSMA therapy. Starting from these data, we performed a predictive dosimetric evaluation of ²²⁵Ac-PSMA to assess the impact of salivary gland protectors in TAT. ²²⁵Ac-PSMA predictive dosimetry was performed in 13 patients treated with ¹⁷⁷Lu-PSMA. Sequential whole-body planar images were acquired 0.5–1, 16–24, 36–48, and 120 h post-injection. ¹⁷⁷Lu time-activity curves were corrected for ²²⁵Ac physical decay and assumed in equilibrium for all daughters. The OLINDA/EXM spherical model was used for dose estimation of the parotid and submandibular glands. The dose for each daughter was calculated and summed for the total dose estimation. The biologically effective dose formalism was extended to high-LET emitters. For the total biologically effective dose formalism extended to high-LET emitters, including the contribution of all daughter isotopes, the brachytherapy formalism for a mixture of radionuclides was implemented. Equivalent doses in 2 Gy/fraction (EQD2) were then calculated and compared with the normal tissue complication probability model derived from external beam radiotherapy for grade ≥2 xerostomia induction. Median predictive doses were 0.86 Bd_RBE5/MBq for parotid glands and 1.05 Bd_RBE5/MBq for submandibular glands, with a 53% reduction compared with previously published data. The results show that the radiobiological model implemented is conservative, as it overestimates the complication rate with respect to the clinical data. Our data shows the possibility of reducing salivary gland uptake in TAT with the coadministration of organ protectors, but these results should be confirmed for TAT with ²²⁵Ac-PSMA by carrying out prospective trials with defined toxicity endpoints and dosimetry procedures.

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification

MOTIVATION

Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed.

OBJECTIVE

This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes.

METHODS

Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined.

RESULTS

Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems.

CONCLUSION

Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.

Analysis of serial CT images for studying the RT effects in head-neck cancer patients

Images taken during and after RT for head and neck cancer have the potential to quantitatively assess xerostomia. Image information may be used as biomarkers of RT effects on parotid glands with significant potential to support adaptive treatment strategies. We investigated the possibility to extract information based on in-room CT images (kVCT, MVCT), acquired for daily image-guided radiotherapy treatment of head-and-neck cancer patients, in order to predict individual response in terms of toxicity. Follow-up MRI images were also used in order to investigate long term parotid gland deformation.